We perform experiments on the photorealistic 3D environments of Gibson [1]} and Matterport3D [2]}. Our proposed method outperforms a state-of-the-art modular RL method [3]} on Gibson with \(7\times \)  lower training cost, and a state-of-the-art end-to-end RL method [4]} on MP3D with \(1@600\times \)  lower training cost. Our method sets the state-of-the-art on the Habitat ObjectNav challenge leaderboard [5]} when compared to published methods.
